Hermes 3 - Llama-3.1 70B offers a robust open-source foundation with a large context window, balancing moderate intelligence with a competitive, albeit slightly higher, pricing structure and slower output speed.
Hermes 3 - Llama-3.1 70B represents a significant offering in the open-weight model landscape, built upon the powerful Llama-3.1 architecture. Developed by Nous Research, this model provides developers with a substantial 70-billion parameter foundation, designed for a wide array of general-purpose text generation and understanding tasks. Its open license fosters innovation and allows for flexible deployment, making it an attractive option for projects seeking transparency and customizability beyond proprietary alternatives. With a generous 128k token context window, Hermes 3 is well-equipped to handle extensive inputs, enabling complex interactions and detailed content processing.
Benchmarked on Deepinfra, Hermes 3 - Llama-3.1 70B demonstrates a balanced profile of capabilities and considerations. While it excels in providing a large context window and maintaining a competitive latency, its intelligence score places it below the average for comparable models, and its output speed is notably slower. This positioning suggests that while it can process large amounts of information, the speed of response and the depth of its analytical reasoning might require careful consideration for time-sensitive or highly complex applications. Its knowledge cutoff in November 2023 ensures it's up-to-date with recent information, a crucial factor for many real-world applications.
From a cost perspective, Hermes 3 - Llama-3.1 70B presents a somewhat mixed picture. Its input token price is slightly above the average for its class, while its output token price is more moderately aligned. This pricing structure, combined with its performance characteristics, positions Hermes 3 as a viable option for developers who prioritize open-source flexibility and large context processing over raw speed or top-tier intelligence, especially when operating within a budget that allows for slightly higher per-token costs. Understanding these trade-offs is key to effectively leveraging Hermes 3 in your AI-powered solutions.
The model's 'non-reasoning' classification indicates its primary strength lies in generating coherent and contextually relevant text rather than performing complex logical deductions or problem-solving that might be expected from more advanced reasoning models. This makes it particularly suitable for tasks like content creation, summarization, conversational AI, and data extraction where pattern recognition and language fluency are paramount. Its open nature also means that with sufficient fine-tuning and domain-specific data, its performance can be significantly enhanced for specialized use cases, unlocking further value for developers willing to invest in customization.
15 (#21 / 33 / 70B)
37 tokens/s
$0.30 /M tokens
$0.30 /M tokens
N/A
0.30 s
| Spec | Details |
|---|---|
| Owner | Nous Research |
| License | Open |
| Context Window | 128k tokens |
| Knowledge Cutoff | November 2023 |
| Base Model | Llama-3.1 |
| Model Size | 70 Billion Parameters |
| Model Type | Open-weight, Non-reasoning |
| Median Output Speed | 37 tokens/s |
| Median Latency | 0.30 seconds |
| Input Token Price | $0.30 / 1M tokens |
| Output Token Price | $0.30 / 1M tokens |
| Intelligence Index | 15 (Rank #21/33) |
| Primary Provider | Deepinfra |
Choosing the right API provider for Hermes 3 - Llama-3.1 70B is crucial for balancing performance, cost, and reliability. Based on current benchmarks, Deepinfra stands out as a primary option, offering a direct pathway to leverage this open-weight model.
| Priority | Pick | Why | Tradeoff to accept |
|---|---|---|---|
| Primary Use | Deepinfra | Why it's a good fit | Key Tradeoff |
| General Purpose & Cost-Efficiency | Deepinfra | Offers direct access to Hermes 3 with transparent pricing and good latency for initial responses. Ideal for projects prioritizing open-source models on a managed platform. | Output speed is notably slower, which can impact real-time applications or high-volume generation. |
| Large Context Processing | Deepinfra | The 128k context window is fully supported, making it suitable for tasks requiring extensive document analysis or long-form content generation. | Higher input token costs compared to some alternatives, which can add up for very large prompts. |
| Open-Source Integration | Deepinfra | Provides a reliable API for an open-weight model, simplifying deployment for developers who value the flexibility and community support of open-source. | Intelligence score is below average, meaning more complex reasoning tasks might require additional prompting or external logic. |
| Development & Prototyping | Deepinfra | Easy to get started and integrate, making it a good choice for experimenting with the Llama-3.1 architecture and quickly iterating on applications. | Performance characteristics (speed, intelligence) may not scale optimally for highly demanding production environments without careful optimization. |
Note: Provider recommendations are based on available benchmark data for Hermes 3 - Llama-3.1 70B on Deepinfra. Performance and pricing may vary with future updates or alternative deployment methods.
Understanding the real-world cost implications of Hermes 3 - Llama-3.1 70B requires analyzing typical usage scenarios. The following examples illustrate estimated costs for common AI tasks, considering the model's specific pricing and performance characteristics on Deepinfra.
| Scenario | Input | Output | What it represents | Estimated cost |
|---|---|---|---|---|
| Scenario | Input (tokens) | Output (tokens) | What it represents | Estimated Cost |
| Long-form Content Generation | 500 (prompt) | 2000 (article) | Generating a detailed blog post or report from a brief outline. | $0.00075 |
| Document Summarization | 10000 (document) | 500 (summary) | Condensing a lengthy research paper or legal document into key points. | $0.00315 |
| Extended Chatbot Interaction | 2000 (conversation history) | 200 (response) | A user engaging in a moderately long conversation with an AI assistant. | $0.00066 |
| Code Explanation & Generation | 1500 (code snippet + query) | 750 (explanation + new code) | Asking the model to explain complex code and suggest improvements. | $0.000675 |
| Data Extraction from Text | 8000 (unstructured data) | 300 (structured output) | Extracting specific entities or facts from a large block of text. | $0.00249 |
| Creative Writing Prompt | 100 (creative prompt) | 1500 (story segment) | Generating a creative story or poem based on a short prompt. | $0.00048 |
These examples highlight that while individual interactions with Hermes 3 - Llama-3.1 70B are generally inexpensive, costs can accumulate quickly with high-volume usage, especially for tasks involving large inputs. The model's slower output speed also means that the total time-to-completion for these tasks might be longer than with faster models, impacting operational efficiency.
Optimizing costs for Hermes 3 - Llama-3.1 70B involves strategic prompting and careful management of token usage. Given its pricing structure and performance characteristics, here are key strategies to maximize efficiency.
Given Hermes 3's slightly higher input token price, it's crucial to make every input token count. Avoid sending unnecessary context or overly verbose instructions.
The model's slower output speed means longer outputs take more time and cost more. Encourage brevity where appropriate.
The 128k context window is a powerful feature, but using it indiscriminately can lead to higher costs. Use it for tasks where deep context is truly beneficial.
Proactive monitoring is essential to prevent unexpected cost overruns, especially with models that have a higher input cost.
Hermes 3 - Llama-3.1 70B is an open-weight large language model developed by Nous Research, based on the Llama-3.1 architecture. It features 70 billion parameters and a 128k token context window, designed for general-purpose text generation and understanding tasks.
Its primary strengths include a very large 128k token context window, its open-source nature providing flexibility for customization, and a competitive time to first token (latency) on Deepinfra. It's well-suited for tasks requiring extensive context processing.
Hermes 3 - Llama-3.1 70B has a below-average intelligence score compared to similar models and a notably slow output speed (37 tokens/s). Its input token price is also somewhat higher than the average for its class, which can increase costs for large inputs.
As a 'non-reasoning' model with a below-average intelligence index, it may struggle with highly complex logical deduction or problem-solving tasks. It's better suited for tasks focused on language generation, summarization, and contextual understanding rather than deep analytical reasoning.
The model's knowledge cutoff is November 2023, meaning it has been trained on data up to that point and may not be aware of events or information that occurred afterward.
On Deepinfra, its input token price ($0.30/M tokens) is somewhat expensive compared to the average ($0.20), while its output token price ($0.30/M tokens) is moderately priced, falling below the average ($0.54). This makes it more cost-effective for applications with high output volume but potentially more expensive for those with very large inputs.
Yes, as an open-weight model, Hermes 3 - Llama-3.1 70B can be fine-tuned on custom datasets. This allows developers to adapt the model to specific domains, improve its performance on niche tasks, and enhance its overall utility for specialized applications.